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Hot Deformation Behavior of Hastelloy C276 Alloy: Microstructural Variation and Constitutive Models.
He, Daoguang; Chen, Shibing; Lin, Yongcheng; Yan, Xintao; Liu, Guan.
Afiliação
  • He D; School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
  • Chen S; State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Changsha 410083, China.
  • Lin Y; School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
  • Yan X; State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Changsha 410083, China.
  • Liu G; School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Materials (Basel) ; 16(18)2023 Sep 13.
Article em En | MEDLINE | ID: mdl-37763471
Isothermal deformation experiments of the Hastelloy C276 alloy were executed using the Gleeble-3500 hot simulator at a temperature range of 1000-1150 °C and a strain rate range of 0.01-10 s-1. Microstructural evolution mechanisms were analyzed via transmission electron microscope (TEM) and electron backscatter diffraction (EBSD). Results reveal that the influences of hot compression parameters on the microstructure variation features and flow behaviors of the Hastelloy C276 alloy were significant. The intense strain hardening (SH) effects caused by the accumulation of substructures were promoted when the strain rates were increased, and true stresses exhibited a notable increasing tendency. However, the apparent DRV effects caused by the annihilation of substructures and the increasingly dynamic recrystallization (DRX) behaviors occurred at high compressed temperature, inducing the reduction in true stresses. In addition, a physical-based (PB) constitutive model and a long short-term memory (LSTM) model optimized using the particle swarm optimization (PSO) algorithm were established to predict the flow behavior of Hastelloy C276 alloy. The smaller average absolute relative error and greater relation coefficient suggest that the LSTM model possesses a higher forecasting accuracy than the PB model.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article